Method used: Drop_seq
Pipeline used to analysize fastq files: zUMIs
library(SingleCellExperiment)
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library(org.Hs.eg.db)
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Import data from zUMIs pipeline output
data_mouse_retina <-
readRDS("~/sc_rnaseq/drop_seq/zUMIs_output/expression/drop_seq_1.dgecounts.rds")
barcode_mouse_retina <-
read_csv("~/sc_rnaseq/drop_seq/zUMIs_output/drop_seq_1kept_barcodes.txt")
## Parsed with column specification:
## cols(
## XC = col_character(),
## n = col_double(),
## cellindex = col_double()
## )
head(barcode_mouse_retina)
## # A tibble: 6 x 3
## XC n cellindex
## <chr> <dbl> <dbl>
## 1 CCAACTCCAAGC 1319237 1
## 2 TTAAGCAGTGGT 975878 2
## 3 GTCCAAAAAAGC 880322 3
## 4 ACGAATCAAGCA 746276 4
## 5 CCTGTCTCTTAT 524240 5
## 6 ACTGTCATTCAA 489592 6
Sanity check between barcode kept and colnames of sparse matrix
length(barcode_mouse_retina$XC) == length(colnames(data_mouse_retina$umicount$exon$all)) ## TRUE
## [1] TRUE
Features names - genes
gene_names <-
rownames(data_mouse_retina$umicount$exon$all)
gene_names[1:10]
## [1] "ENSMUSG00000000001.4" "ENSMUSG00000000028.15" "ENSMUSG00000000031.16"
## [4] "ENSMUSG00000000037.17" "ENSMUSG00000000049.11" "ENSMUSG00000000056.7"
## [7] "ENSMUSG00000000058.6" "ENSMUSG00000000078.7" "ENSMUSG00000000085.16"
## [10] "ENSMUSG00000000088.7"
Rownames are in ensemble id
rownames(data_mouse_retina$umicount$exon$all)[1:10]
## [1] "ENSMUSG00000000001.4" "ENSMUSG00000000028.15" "ENSMUSG00000000031.16"
## [4] "ENSMUSG00000000037.17" "ENSMUSG00000000049.11" "ENSMUSG00000000056.7"
## [7] "ENSMUSG00000000058.6" "ENSMUSG00000000078.7" "ENSMUSG00000000085.16"
## [10] "ENSMUSG00000000088.7"
Changing them to external gene name
gene_names are in ensembl id, so retriving respective gene names of ensembl id
Check list of available of dataset
listMarts() ## list of ensamble version
## biomart version
## 1 ENSEMBL_MART_ENSEMBL Ensembl Genes 100
## 2 ENSEMBL_MART_MOUSE Mouse strains 100
## 3 ENSEMBL_MART_SNP Ensembl Variation 100
## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 100
listEnsembl()
## biomart version
## 1 ensembl Ensembl Genes 100
## 2 ENSEMBL_MART_MOUSE Mouse strains 100
## 3 snp Ensembl Variation 100
## 4 regulation Ensembl Regulation 100
First ensembl_100 is for mart argument in useDatasets function
ensembl_100 <-
useEnsembl("ensembl") ## here using default version 100 because genecode 34 is based on ensemble 100 ```
head(listDatasets(ensembl_100)) ## show list of database avaialble for all species
## dataset description
## 1 acalliptera_gene_ensembl Eastern happy genes (fAstCal1.2)
## 2 acarolinensis_gene_ensembl Anole lizard genes (AnoCar2.0)
## 3 acchrysaetos_gene_ensembl Golden eagle genes (bAquChr1.2)
## 4 acitrinellus_gene_ensembl Midas cichlid genes (Midas_v5)
## 5 amelanoleuca_gene_ensembl Panda genes (ailMel1)
## 6 amexicanus_gene_ensembl Mexican tetra genes (Astyanax_mexicanus-2.0)
## version
## 1 fAstCal1.2
## 2 AnoCar2.0
## 3 bAquChr1.2
## 4 Midas_v5
## 5 ailMel1
## 6 Astyanax_mexicanus-2.0
## here we are interested in "mmusculus_gene_ensembl" dataset
Loading mouse ensembl
ensembl_100_mouse <-
useDataset("mmusculus_gene_ensembl", mart = ensembl_100) ## loading mouse ensamble
List ensembl attributes
head(listAttributes(ensembl_100_mouse)) ## list all attributes avaialble for ensembl_100_mouse
## name description page
## 1 ensembl_gene_id Gene stable ID feature_page
## 2 ensembl_gene_id_version Gene stable ID version feature_page
## 3 ensembl_transcript_id Transcript stable ID feature_page
## 4 ensembl_transcript_id_version Transcript stable ID version feature_page
## 5 ensembl_peptide_id Protein stable ID feature_page
## 6 ensembl_peptide_id_version Protein stable ID version feature_page
head(listFilters(ensembl_100_mouse)) ## list all filters avaialble for ensembl_100_mouse
## name description
## 1 chromosome_name Chromosome/scaffold name
## 2 start Start
## 3 end End
## 4 strand Strand
## 5 chromosomal_region e.g. 1:100:10000:-1, 1:100000:200000:1
## 6 with_ccds With CCDS ID(s)
Getting attributes using biomaRT
annotation_mouse_genes <-
biomaRt::getBM(attributes = c("ensembl_gene_id", "ensembl_gene_id_version",
"ensembl_transcript_id", "ensembl_transcript_id_version",
"external_gene_name"),
filters = c("ensembl_gene_id_version"), ## filters ensemble gene_id_version because it has ENSMUSG00000007050.17
values = gene_names,
mart = ensembl_100_mouse) ## multiple transcipt with one gene
#annotation_mouse_genes ## this table has higher number of rows compared to gene names because transcript_id_version (alternative splicing)
names(annotation_mouse_genes)
## [1] "ensembl_gene_id" "ensembl_gene_id_version"
## [3] "ensembl_transcript_id" "ensembl_transcript_id_version"
## [5] "external_gene_name"
head(annotation_mouse_genes)
## ensembl_gene_id ensembl_gene_id_version ensembl_transcript_id
## 1 ENSMUSG00000001506 ENSMUSG00000001506.10 ENSMUST00000001547
## 2 ENSMUSG00000001506 ENSMUSG00000001506.10 ENSMUST00000139974
## 3 ENSMUSG00000001506 ENSMUSG00000001506.10 ENSMUST00000148593
## 4 ENSMUSG00000001506 ENSMUSG00000001506.10 ENSMUST00000148046
## 5 ENSMUSG00000000708 ENSMUSG00000000708.14 ENSMUST00000163648
## 6 ENSMUSG00000000708 ENSMUSG00000000708.14 ENSMUST00000000724
## ensembl_transcript_id_version external_gene_name
## 1 ENSMUST00000001547.7 Col1a1
## 2 ENSMUST00000139974.1 Col1a1
## 3 ENSMUST00000148593.1 Col1a1
## 4 ENSMUST00000148046.1 Col1a1
## 5 ENSMUST00000163648.1 Kat2b
## 6 ENSMUST00000000724.14 Kat2b
nrow(annotation_mouse_genes)
## [1] 111688
Converting dataframe to tibble for downstream analysis
annotation_mouse_genes_gene_name <- ## change in to tibble
as_tibble(annotation_mouse_genes) %>%
dplyr::select(ensembl_gene_id, ensembl_gene_id_version, external_gene_name) %>%
distinct(ensembl_gene_id_version, .keep_all = T) %>% ## keeping only ditinct
distinct(external_gene_name, .keep_all = T) ## removing dplicated gene names
annotation_mouse_genes_gene_name
## # A tibble: 31,808 x 3
## ensembl_gene_id ensembl_gene_id_version external_gene_name
## <chr> <chr> <chr>
## 1 ENSMUSG00000001506 ENSMUSG00000001506.10 Col1a1
## 2 ENSMUSG00000000708 ENSMUSG00000000708.14 Kat2b
## 3 ENSMUSG00000000792 ENSMUSG00000000792.2 Slc5a5
## 4 ENSMUSG00000001228 ENSMUSG00000001228.14 Uhrf1
## 5 ENSMUSG00000000957 ENSMUSG00000000957.11 Mmp14
## 6 ENSMUSG00000000530 ENSMUSG00000000530.16 Acvrl1
## 7 ENSMUSG00000001844 ENSMUSG00000001844.10 Zdhhc4
## 8 ENSMUSG00000000409 ENSMUSG00000000409.14 Lck
## 9 ENSMUSG00000000532 ENSMUSG00000000532.11 Acvr1b
## 10 ENSMUSG00000000531 ENSMUSG00000000531.5 Grasp
## # … with 31,798 more rows
First order and subset based on gene name to assign gene names to ensembl id
count_matrix_ordered <-
data_mouse_retina$umicount$exon$all[sort(annotation_mouse_genes_gene_name$ensembl_gene_id_version),]
sort(c("ENSMUSG00000001506.10", "ENSMUSG00000000708.14", "ENSMUSG00000000792.2", "ENSMUSG00000001228.14"))
## [1] "ENSMUSG00000000708.14" "ENSMUSG00000000792.2" "ENSMUSG00000001228.14"
## [4] "ENSMUSG00000001506.10"
head(rownames(count_matrix_ordered))
## [1] "ENSMUSG00000000001.4" "ENSMUSG00000000028.15" "ENSMUSG00000000031.16"
## [4] "ENSMUSG00000000037.17" "ENSMUSG00000000049.11" "ENSMUSG00000000056.7"
Do same for external gene id
external_gene_names_ordered <- ## changing to vector
annotation_mouse_genes_gene_name %>%
arrange(ensembl_gene_id_version) %>%
dplyr::select(external_gene_name) %>%
unlist(.) %>%
as.vector()
head(external_gene_names_ordered)
## [1] "Gnai3" "Cdc45" "H19" "Scml2" "Apoh" "Narf"
Sanity check before changing ensembl id to gene names
which (annotation_mouse_genes_gene_name %>%
arrange(ensembl_gene_id_version) %>%
dplyr::select(ensembl_gene_id_version) %>%
unlist(.) %>%
as.vector() != rownames(count_matrix_ordered)) ## has to be zero
## integer(0)
#another sanity check
matrix_row_names_check <-
annotation_mouse_genes_gene_name %>%
arrange(ensembl_gene_id_version) %>%
dplyr::select(ensembl_gene_id_version, external_gene_name) %>%
slice(1:5) %>%
mutate(matrix_rowname = rownames(count_matrix_ordered)[1:5])
matrix_row_names_check ## everthing is fine
## # A tibble: 5 x 3
## ensembl_gene_id_version external_gene_name matrix_rowname
## <chr> <chr> <chr>
## 1 ENSMUSG00000000001.4 Gnai3 ENSMUSG00000000001.4
## 2 ENSMUSG00000000028.15 Cdc45 ENSMUSG00000000028.15
## 3 ENSMUSG00000000031.16 H19 ENSMUSG00000000031.16
## 4 ENSMUSG00000000037.17 Scml2 ENSMUSG00000000037.17
## 5 ENSMUSG00000000049.11 Apoh ENSMUSG00000000049.11
finally assign external gene name to ensembl id
rownames(count_matrix_ordered) <- external_gene_names_ordered
Rest of the analysis will be carried out by using Seurat package
Now creating seurat object
keeping seurat object with all genes expressed in >= 3cells and atleast 100 genes
seurat_mouse_retina <-
CreateSeuratObject(counts = count_matrix_ordered,
project = "mouse_retina",
assay = "RNA",
min.cells =3,
min.features = 100)
seurat_mouse_retina
## An object of class Seurat
## 25301 features across 16406 samples within 1 assay
## Active assay: RNA (25301 features, 0 variable features)
class(seurat_mouse_retina)
## [1] "Seurat"
## attr(,"package")
## [1] "Seurat"
head(seurat_mouse_retina@meta.data)
## orig.ident nCount_RNA nFeature_RNA
## AAAAAAAAAAAA mouse_retina 458 441
## AAAAAACCCGAT mouse_retina 449 313
## AAAAAACTTTCT mouse_retina 661 397
## AAAAAAGCATGG mouse_retina 345 225
## AAAAAATAGATG mouse_retina 493 343
## AAAAAATGATCT mouse_retina 439 301
Calculating number of genes detected per UMI
seurat_mouse_retina$log10_genes_perumi <- ## detects few genes expressing higher counts
log10(seurat_mouse_retina$nFeature_RNA) / log10(seurat_mouse_retina$nCount_RNA)
Mitochondrial genes to find out dead cell percentage
mitochondrial_genes <-
annotation_mouse_genes_gene_name$external_gene_name[grep("^mt", annotation_mouse_genes_gene_name$external_gene_name)]
seurat_mouse_retina$mit_ratio <-
PercentageFeatureSet(object = seurat_mouse_retina,pattern = "^mt") ## in percentages
seurat_mouse_retina$mit_ratio <-
seurat_mouse_retina@meta.data$mit_ratio/100 ## back to RA (relative abundance= mt/total features)
head(seurat_mouse_retina$mit_ratio)
## AAAAAAAAAAAA AAAAAACCCGAT AAAAAACTTTCT AAAAAAGCATGG AAAAAATAGATG AAAAAATGATCT
## 0.002183406 0.013363029 0.130105900 0.115942029 0.048681542 0.077448747
length(which(seurat_mouse_retina@meta.data$mit_ratio < 0.1))
## [1] 13945
nrow(seurat_mouse_retina@meta.data)
## [1] 16406
Another way to calculate mitochondrial ratio using basic r functions
mito_ratio <-
Matrix::colSums(seurat_mouse_retina@assays[["RNA"]][mitochondrial_genes])/Matrix::colSums(seurat_mouse_retina@assays[["RNA"]])
Saving file
#saveRDS(seurat_mouse_retina, "~/sc_rnaseq/drop_seq/r_analysis/seurat_mouse_retina.rds")
Quality control violin plot of number of cells, rna counts and mitochondria ratio
VlnPlot(object = seurat_mouse_retina,
features = c("nFeature_RNA", "nCount_RNA", "mit_ratio"), ncol = 3)
Total RNA counts (transcripts) per cell density plot
umi_count_per_cell <-
seurat_mouse_retina@meta.data %>%
ggplot(aes(x=nCount_RNA)) +
geom_density(alpha = 0.2, color="#ef8a62", fill="#ef8a62") +
scale_x_log10() +
theme_classic() +
ylab("Cell density") +
geom_vline(xintercept = 5000)+
xlab("nUMI")
umi_count_per_cell
Genes per cell
genes_per_cell <-
seurat_mouse_retina@meta.data %>%
ggplot(aes(x=nFeature_RNA)) +
geom_density(alpha = 0.2, color="#ef8a62", fill="#ef8a62") +
scale_x_log10() +
theme_classic() +
ylab("Cell density") +
geom_vline(xintercept = 2500)+
xlab("nGene")
genes_per_cell
Total count and genes detected
umi_genes_plot <-
seurat_mouse_retina@meta.data %>%
ggplot(aes(x = nCount_RNA, y=nFeature_RNA)) +
geom_point(color="#ef8a62")+
scale_x_log10() +
scale_y_log10()+
geom_smooth(method = lm) +
theme_classic() +
geom_text(x = 2.5, y = 4, label = "Pearson = 0.94")
umi_genes_plot
## `geom_smooth()` using formula 'y ~ x'
cor(seurat_mouse_retina@meta.data$nCount_RNA, seurat_mouse_retina@meta.data$nFeature_RNA)
## [1] 0.9408101
Complexity (log10(counts/genes))
complexity_plot <- ## detect few genes expressing higher counts
seurat_mouse_retina@meta.data %>%
ggplot(aes(x =log10_genes_perumi)) +
geom_density(fill="#ef8a62")+
theme_classic() +
geom_vline(xintercept = 0.87)
complexity_plot
Total counts- mitochondrial plot
umi_mito_plot <-
seurat_mouse_retina@meta.data %>%
ggplot(aes(x = nCount_RNA, y=mit_ratio)) +
geom_point(color="#ef8a62")+
scale_x_log10() +
#scale_y_log10()+
#geom_smooth(method = lm) +
theme_classic() +
geom_hline(yintercept = 0.2)
umi_mito_plot
Filtering based on following criteria
filtered_seurat <-
subset(x =seurat_mouse_retina,
subset = (nCount_RNA <5000) &
(nFeature_RNA <2500) &
(log10_genes_perumi >0.87) &
(mit_ratio <0.2))
#saveRDS(filtered_seurat, "~/sc_rnaseq/drop_seq/r_analysis/filtered_seurat.rds")
filtered_seurat <- readRDS("~/sc_rnaseq/drop_seq/r_analysis/filtered_seurat.rds")
Quality control is done but before doing cell clustering, we having to check whether clustering could be influenced of cell cycle differentiation * Downloading cellcycle genes
Cell cycle scoring
mouse_cell_cycle_file <-
getURL("https://raw.githubusercontent.com/hbc/tinyatlas/master/cell_cycle/Mus_musculus.csv")
mouse_cell_cycle_file
## [1] "phase,geneID,modified\r\nG2/M,ENSMUSG00000001403,9/13/17\r\nG2/M,ENSMUSG00000004880,9/13/17\r\nG2/M,ENSMUSG00000005698,9/13/17\r\nG2/M,ENSMUSG00000006398,9/13/17\r\nG2/M,ENSMUSG00000009575,9/13/17\r\nG2/M,ENSMUSG00000012443,9/13/17\r\nG2/M,ENSMUSG00000015749,9/13/17\r\nG2/M,ENSMUSG00000017716,9/13/17\r\nG2/M,ENSMUSG00000019942,9/13/17\r\nG2/M,ENSMUSG00000019961,9/13/17\r\nG2/M,ENSMUSG00000020330,9/13/17\r\nG2/M,ENSMUSG00000020737,9/13/17\r\nG2/M,ENSMUSG00000020808,9/13/17\r\nG2/M,ENSMUSG00000020897,9/13/17\r\nG2/M,ENSMUSG00000020914,9/13/17\r\nG2/M,ENSMUSG00000022385,9/13/17\r\nG2/M,ENSMUSG00000022391,9/13/17\r\nG2/M,ENSMUSG00000023505,9/13/17\r\nG2/M,ENSMUSG00000024056,9/13/17\r\nG2/M,ENSMUSG00000024795,9/13/17\r\nG2/M,ENSMUSG00000026605,9/13/17\r\nG2/M,ENSMUSG00000026622,9/13/17\r\nG2/M,ENSMUSG00000026683,9/13/17\r\nG2/M,ENSMUSG00000027306,9/13/17\r\nG2/M,ENSMUSG00000027379,9/13/17\r\nG2/M,ENSMUSG00000027469,9/13/17\r\nG2/M,ENSMUSG00000027496,9/13/17\r\nG2/M,ENSMUSG00000027699,9/13/17\r\nG2/M,ENSMUSG00000028044,9/13/17\r\nG2/M,ENSMUSG00000028678,9/13/17\r\nG2/M,ENSMUSG00000028873,9/13/17\r\nG2/M,ENSMUSG00000029177,9/13/17\r\nG2/M,ENSMUSG00000031004,9/13/17\r\nG2/M,ENSMUSG00000032218,9/13/17\r\nG2/M,ENSMUSG00000032254,9/13/17\r\nG2/M,ENSMUSG00000034349,9/13/17\r\nG2/M,ENSMUSG00000035293,9/13/17\r\nG2/M,ENSMUSG00000036752,9/13/17\r\nG2/M,ENSMUSG00000036777,9/13/17\r\nG2/M,ENSMUSG00000037313,9/13/17\r\nG2/M,ENSMUSG00000037544,9/13/17\r\nG2/M,ENSMUSG00000037725,9/13/17\r\nG2/M,ENSMUSG00000038252,9/13/17\r\nG2/M,ENSMUSG00000038379,9/13/17\r\nG2/M,ENSMUSG00000040549,9/13/17\r\nG2/M,ENSMUSG00000044201,9/13/17\r\nG2/M,ENSMUSG00000044783,9/13/17\r\nG2/M,ENSMUSG00000045328,9/13/17\r\nG2/M,ENSMUSG00000048327,9/13/17\r\nG2/M,ENSMUSG00000048922,9/13/17\r\nG2/M,ENSMUSG00000054717,9/13/17\r\nG2/M,ENSMUSG00000062248,9/13/17\r\nG2/M,ENSMUSG00000068744,9/13/17\r\nG2/M,ENSMUSG00000074802,9/13/17\r\nS,ENSMUSG00000000028,9/13/17\r\nS,ENSMUSG00000001228,9/13/17\r\nS,ENSMUSG00000002870,9/13/17\r\nS,ENSMUSG00000004642,9/13/17\r\nS,ENSMUSG00000005410,9/13/17\r\nS,ENSMUSG00000006678,9/13/17\r\nS,ENSMUSG00000006715,9/13/17\r\nS,ENSMUSG00000017499,9/13/17\r\nS,ENSMUSG00000020649,9/13/17\r\nS,ENSMUSG00000022360,9/13/17\r\nS,ENSMUSG00000022422,9/13/17\r\nS,ENSMUSG00000022673,9/13/17\r\nS,ENSMUSG00000022945,9/13/17\r\nS,ENSMUSG00000023104,9/13/17\r\nS,ENSMUSG00000024151,9/13/17\r\nS,ENSMUSG00000024742,9/13/17\r\nS,ENSMUSG00000025001,9/13/17\r\nS,ENSMUSG00000025395,9/13/17\r\nS,ENSMUSG00000025747,9/13/17\r\nS,ENSMUSG00000026355,9/13/17\r\nS,ENSMUSG00000027242,9/13/17\r\nS,ENSMUSG00000027323,9/13/17\r\nS,ENSMUSG00000027342,9/13/17\r\nS,ENSMUSG00000028212,9/13/17\r\nS,ENSMUSG00000028282,9/13/17\r\nS,ENSMUSG00000028560,9/13/17\r\nS,ENSMUSG00000028693,9/13/17\r\nS,ENSMUSG00000028884,9/13/17\r\nS,ENSMUSG00000029591,9/13/17\r\nS,ENSMUSG00000030346,9/13/17\r\nS,ENSMUSG00000030528,9/13/17\r\nS,ENSMUSG00000030726,9/13/17\r\nS,ENSMUSG00000030978,9/13/17\r\nS,ENSMUSG00000031629,9/13/17\r\nS,ENSMUSG00000031821,9/13/17\r\nS,ENSMUSG00000032397,9/13/17\r\nS,ENSMUSG00000034329,9/13/17\r\nS,ENSMUSG00000037474,9/13/17\r\nS,ENSMUSG00000039748,9/13/17\r\nS,ENSMUSG00000041712,9/13/17\r\nS,ENSMUSG00000042489,9/13/17\r\nS,ENSMUSG00000046179,9/13/17\r\nS,ENSMUSG00000055612,9/13/17"
mouse_cell_cycle_file_1 <- ## merging with external gene name
read_csv(mouse_cell_cycle_file) %>%
inner_join(., annotation_mouse_genes_gene_name[, c("ensembl_gene_id", "external_gene_name")],
by = c("geneID" = "ensembl_gene_id"))
unique(mouse_cell_cycle_file_1$phase)
## [1] "G2/M" "S"
Selecting mouse_g2m cell cycle genes
mouse_g2m_genes <-
mouse_cell_cycle_file_1 %>%
filter(phase == "G2/M") %>%
dplyr::select(external_gene_name) %>%
unlist(.) %>%
as.vector()
Selecting mouse_S genes
mouse_S_genes <-
mouse_cell_cycle_file_1 %>%
filter(phase == "S") %>%
dplyr::select(external_gene_name) %>%
unlist(.) %>%
as.vector()
Now we have cell cycle genes and before cell cycle clustering, lets to normalization
Normalizing data for cell cycle scoring
filtered_seurat_log_normalize <-
NormalizeData(filtered_seurat) ## by default lognormalization method
cell_phase_seurat <- ## this automatically add two more cycle columns in metadata
CellCycleScoring(filtered_seurat_log_normalize,
g2m.features = mouse_g2m_genes,
s.features = mouse_S_genes)
## Warning: The following features are not present in the object: Chaf1b, not
## searching for symbol synonyms
## Warning: The following features are not present in the object: Gtse1, not
## searching for symbol synonyms
head(cell_phase_seurat@meta.data)
## orig.ident nCount_RNA nFeature_RNA mit_ratio
## AAAAAAAAAAAA mouse_retina 458 441 0.002183406
## AAAAAACCCGAT mouse_retina 449 313 0.013363029
## AAAAAACTTTCT mouse_retina 661 397 0.130105900
## AAAAAAGCATGG mouse_retina 345 225 0.115942029
## AAAAAATAGATG mouse_retina 493 343 0.048681542
## AAAAAATGATCT mouse_retina 439 301 0.077448747
## log10_genes_perumi S.Score G2M.Score Phase
## AAAAAAAAAAAA 0.9938265 -0.04363240 0.003434444 G2M
## AAAAAACCCGAT 0.9409173 -0.01990009 -0.045968520 G1
## AAAAAACTTTCT 0.9214911 0.03377475 -0.002935929 S
## AAAAAAGCATGG 0.9268519 -0.01484617 -0.043451291 G1
## AAAAAATAGATG 0.9414921 -0.02636794 0.011516264 G2M
## AAAAAATGATCT 0.9379753 -0.02685283 -0.055177337 G1
cell_phase_seurat
## An object of class Seurat
## 25301 features across 15678 samples within 1 assay
## Active assay: RNA (25301 features, 0 variable features)
Finding high variable genes
cell_phase_seurat <-
FindVariableFeatures(cell_phase_seurat,
selection.method = "vst",
nfeatures = 2000,
verbose = F)
cell_phase_seurat ## 2000 variable added to the object
## An object of class Seurat
## 25301 features across 15678 samples within 1 assay
## Active assay: RNA (25301 features, 2000 variable features)
cell_phase_seurat <- ScaleData(cell_phase_seurat) ## scaling data
## Centering and scaling data matrix
cell_phase_seurat@assays[["RNA"]][1:10] ## scaled data floating numbers
## 10 x 15678 sparse Matrix of class "dgCMatrix"
## [[ suppressing 33 column names 'AAAAAAAAAAAA', 'AAAAAACCCGAT', 'AAAAAACTTTCT' ... ]]
##
## Gnai3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## Cdc45 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## H19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## Scml2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## Apoh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## Narf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## Cav2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## Klf6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## Scmh1 . 3.147239 . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## Cox5a . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.591755 .
##
## Gnai3 . . . ......
## Cdc45 . . . ......
## H19 . . . ......
## Scml2 . . . ......
## Apoh . . . ......
## Narf . . . ......
## Cav2 . . . ......
## Klf6 . . . ......
## Scmh1 . . . ......
## Cox5a . . . ......
##
## .....suppressing 15645 columns in show(); maybe adjust 'options(max.print= *, width = *)'
## ..............................
cell_phase_seurat
## An object of class Seurat
## 25301 features across 15678 samples within 1 assay
## Active assay: RNA (25301 features, 2000 variable features)
SCT transformation and Cell clustering using UMAP
cell_phase_seurat <-
SCTransform(cell_phase_seurat, vars.to.regress = c("mit_ratio"))
## Calculating cell attributes for input UMI matrix
## Variance stabilizing transformation of count matrix of size 21757 by 15678
## Model formula is y ~ log_umi
## Get Negative Binomial regression parameters per gene
## Using 2000 genes, 15678 cells
##
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##
|
|================== | 25%
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##
|
|========================== | 38%
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##
|
|=================================== | 50%
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##
|
|============================================ | 62%
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##
|
|==================================================== | 75%
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##
|
|============================================================= | 88%
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##
|
|======================================================================| 100%
## Second step: Get residuals using fitted parameters for 21757 genes
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## Computing corrected count matrix for 21757 genes
##
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## Calculating gene attributes
## Wall clock passed: Time difference of 5.673105 mins
## Determine variable features
## Set 3000 variable features
## Place corrected count matrix in counts slot
## Regressing out mit_ratio
## Centering data matrix
## Set default assay to SCT
cell_phase_seurat ## now sct assay is adding to the object
## An object of class Seurat
## 47058 features across 15678 samples within 2 assays
## Active assay: SCT (21757 features, 3000 variable features)
## 1 other assay present: RNA
cell_phase_seurat <-
RunPCA(cell_phase_seurat)
## PC_ 1
## Positive: Trpm1, Rpgrip1, Rp1, Pde6g, Mir124a-1hg, Calm1, Fam161a, Map1b, Isl1, Pde6h
## Chgb, Mak, Ankrd12, Pcp4, Atp2b1, Rabgef1, Dmd, Cep290, Prkca, Slc4a7
## Unc13b, Gngt2, Amer2, Glmn, Lrtm1, Wdr17, Scg2, Samd7, Opn1sw, Pdzph1
## Negative: Apoe, Glul, Clu, Acsl3, Dkk3, Rlbp1, Sparc, Slc1a3, Cp, Jun
## Col9a1, Aqp4, Fos, Ccn1, Spc25, Ptn, Car14, Id3, Hes1, Gpr37
## Spon1, Cd9, Zfp36l1, Egr1, Abca8a, Dbi, Plpp3, Pdpn, Kdr, Mfge8
## PC_ 2
## Positive: Rpgrip1, Rp1, Mir124a-1hg, Pde6g, Fam161a, Pde6h, Mak, Dmd, Slc4a7, Rabgef1
## Opn1sw, Cst3, Map1b, Wdr17, Opn1mw, Pdzph1, Samd7, Amer2, Syne2, Glmn
## Sntb2, Marchf1, Cep290, Fos, Arr3, Dyrk2, A430035B10Rik, Gnat2, Gucy2f, Gngt2
## Negative: Trpm1, Calm1, Isl1, Pcp4, Gnao1, Car8, Prkca, Ndnf, Lrtm1, Chgb
## Meg3, Scg2, Kcnma1, Prox1, Cabp5, Fam135b, Zbtb20, Ablim1, Tgfb2, Atp2b1
## Snhg11, Gabra1, Ttc3, Gm28437, Atp1b1, Xist, Gria2, App, Scgn, Igf1
## PC_ 3
## Positive: Snhg11, Atp1b1, Sparcl1, Meg3, Tfap2b, Slc6a1, Gria2, Spock3, App, Lsamp
## Gabra2, Igfbp7, Gad1, Pclo, Nhlh2, Ttc3, C1ql1, Gad2, Scg2, Stmn2
## Calb2, Gria3, Id4, Cdk14, AI593442, Thy1, Cxcl14, Slc8a1, Nefl, Fasn
## Negative: Pde6h, Opn1sw, Opn1mw, Gngt2, Gnat2, Arr3, Kcne2, Pde6c, Trpm1, Thrb
## Calm1, Slc24a2, Isl1, Chgb, Gm28437, mt-Cytb, Acsl3, Mfge8, Clca1, Lrtm1
## Atp2b1, mt-Nd5, Hk2, Car8, Cabp5, Ndnf, Prkca, Btg2, Krt18, Cdk6
## PC_ 4
## Positive: Trpm1, Isl1, Prkca, Fos, Rp1, Rpgrip1, Car8, Ndnf, Zbtb20, Mir124a-1hg
## Calm1, Jun, Lrtm1, Glul, Fam161a, Egr1, Fam135b, Ccn1, Ablim1, Prox1
## Cabp5, Fosb, Unc13b, Pde6g, Rlbp1, Rabgef1, Dmd, Slc4a7, Spc25, Cep290
## Negative: Snhg11, Atp1b1, Pde6h, Meg3, Sparcl1, Tfap2b, Slc6a1, Ttc3, Spock3, Opn1sw
## App, Opn1mw, Gngt2, Gria2, Scg2, Gnat2, Igfbp7, Lsamp, Gabra2, Gad1
## C1ql1, Cxcl14, Arr3, Cplx2, Cd47, Thy1, Gria3, Stmn2, Calb2, Gad2
## PC_ 5
## Positive: Snhg11, Atp1b1, Meg3, Tfap2b, Slc6a1, Dkk3, Gria2, Glul, Spock3, Ttc3
## Scg2, Acsl3, Rlbp1, Lsamp, Gabra2, Gad1, Clu, C1ql1, Gad2, Cxcl14
## Car2, Cbln2, Nhlh2, AI593442, Cplx2, Id4, Thy1, Spc25, Gria3, Npnt
## Negative: Igfbp7, Sparc, Ptprb, Cldn5, Ly6c1, Col4a1, Adgrl4, Ctla2a, Cd93, Abcb1a
## Itm2a, Sparcl1, Slco1a4, Fn1, Lama4, Bsg, Tm4sf1, Cdh5, Ramp2, Itga1
## Pecam1, Anxa3, Ifitm3, Ly6c2, Ets1, Adgrf5, Flt1, Eng, Klf2, B2m
cell_phase_seurat <-
RunUMAP(cell_phase_seurat,
dims = 1:40,
reduction = "pca")
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 11:22:22 UMAP embedding parameters a = 0.9922 b = 1.112
## 11:22:22 Read 15678 rows and found 40 numeric columns
## 11:22:22 Using Annoy for neighbor search, n_neighbors = 30
## 11:22:22 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 11:22:26 Writing NN index file to temp file /tmp/RtmpXk929N/file719d74d79035
## 11:22:26 Searching Annoy index using 1 thread, search_k = 3000
## 11:22:32 Annoy recall = 100%
## 11:22:33 Commencing smooth kNN distance calibration using 1 thread
## 11:22:34 Initializing from normalized Laplacian + noise
## 11:22:35 Commencing optimization for 200 epochs, with 755572 positive edges
## 11:22:43 Optimization finished
cell_phase_seurat ## umap added to the object
## An object of class Seurat
## 47058 features across 15678 samples within 2 assays
## Active assay: SCT (21757 features, 3000 variable features)
## 1 other assay present: RNA
## 2 dimensional reductions calculated: pca, umap
DimPlot(cell_phase_seurat, reduction = "umap")
UAMP plot by cell cycle phase
cell_cycle_umap_plot <-
DimPlot(cell_phase_seurat,
group.by = "Phase")
cell_cycle_umap_plot
Cell_cycle_umap_plot looks good and no specific clustering by cell cycle phase
Find clusters
cell_phase_seurat <-
FindNeighbors(object = cell_phase_seurat,
dims = 1:40) # using 40 dimensions
## Computing nearest neighbor graph
## Computing SNN
cell_phase_seurat
## An object of class Seurat
## 47058 features across 15678 samples within 2 assays
## Active assay: SCT (21757 features, 3000 variable features)
## 1 other assay present: RNA
## 2 dimensional reductions calculated: pca, umap
cell_phase_seurat <-
FindClusters(object = cell_phase_seurat,
resolution = c(0.4, 0.6, 0.8))
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 15678
## Number of edges: 754734
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8777
## Number of communities: 18
## Elapsed time: 3 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 15678
## Number of edges: 754734
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8448
## Number of communities: 22
## Elapsed time: 2 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 15678
## Number of edges: 754734
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8145
## Number of communities: 26
## Elapsed time: 2 seconds
head(cell_phase_seurat@meta.data) ## culsters are added to the figure
## orig.ident nCount_RNA nFeature_RNA mit_ratio
## AAAAAAAAAAAA mouse_retina 458 441 0.002183406
## AAAAAACCCGAT mouse_retina 449 313 0.013363029
## AAAAAACTTTCT mouse_retina 661 397 0.130105900
## AAAAAAGCATGG mouse_retina 345 225 0.115942029
## AAAAAATAGATG mouse_retina 493 343 0.048681542
## AAAAAATGATCT mouse_retina 439 301 0.077448747
## log10_genes_perumi S.Score G2M.Score Phase nCount_SCT
## AAAAAAAAAAAA 0.9938265 -0.04363240 0.003434444 G2M 445
## AAAAAACCCGAT 0.9409173 -0.01990009 -0.045968520 G1 452
## AAAAAACTTTCT 0.9214911 0.03377475 -0.002935929 S 624
## AAAAAAGCATGG 0.9268519 -0.01484617 -0.043451291 G1 382
## AAAAAATAGATG 0.9414921 -0.02636794 0.011516264 G2M 492
## AAAAAATGATCT 0.9379753 -0.02685283 -0.055177337 G1 445
## nFeature_SCT SCT_snn_res.0.4 SCT_snn_res.0.6 SCT_snn_res.0.8
## AAAAAAAAAAAA 428 9 9 9
## AAAAAACCCGAT 313 0 0 0
## AAAAAACTTTCT 394 5 4 7
## AAAAAAGCATGG 224 1 1 1
## AAAAAATAGATG 342 0 0 0
## AAAAAATGATCT 301 12 13 14
## seurat_clusters
## AAAAAAAAAAAA 9
## AAAAAACCCGAT 0
## AAAAAACTTTCT 7
## AAAAAAGCATGG 1
## AAAAAATAGATG 0
## AAAAAATGATCT 14
Stick with 0.4 resolution because we expect less number of cluster.
For example 6 major cell types expected from mouse retinal data
Seurat::Idents(object = cell_phase_seurat) <- "SCT_snn_res.0.4"
cell_clusters_plot <-
DimPlot(cell_phase_seurat,
reduction = "umap",
label = T,
label.size = 6)
cell_clusters_plot
# UMAPPlot(cell_phase_seurat) another way to do it- same plot
Cluster based on 0.6 resolution
Seurat::Idents(object = cell_phase_seurat) <- "SCT_snn_res.0.6"
cell_clusters_plot_0.6 <-
DimPlot(cell_phase_seurat,
reduction = "umap",
label = T,
label.size = 6)
cell_clusters_plot_0.6
UMAPPlot(cell_phase_seurat)
Number of cluster increased from 17 to 21 by increasing resolution from 0.4 to 0.6
Seurat::Idents(object = cell_phase_seurat) <- "SCT_snn_res.0.4"
Save data
#saveRDS(cell_phase_seurat, "~/sc_rnaseq/drop_seq/r_analysis/cell_phase_seurat.rds")
Distribution of cells per cluster
cells_per_cluster <-
FetchData(cell_phase_seurat,
vars = "ident") %>% ## ident = cluster assignment to respective cell
dplyr::count(ident) %>%
tidyr::spread(ident, n)
cells_per_cluster
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 1 5074 3088 1019 843 820 790 724 521 430 422 403 378 314 231 227 156 151 87
Another way to do- distribution of cells per cluster
cell_phase_seurat@meta.data %>%
dplyr::select(SCT_snn_res.0.4) %>%
group_by(SCT_snn_res.0.4) %>%
count() %>%
ungroup() %>%
spread(SCT_snn_res.0.4, n)
## # A tibble: 1 x 18
## `0` `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11` `12`
## <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 5074 3088 1019 843 820 790 724 521 430 422 403 378 314
## # … with 5 more variables: `13` <int>, `14` <int>, `15` <int>, `16` <int>,
## # `17` <int>
List of markers genes for cell types
cell_type_markers <-
read_tsv("~/sc_rnaseq/drop_seq/r_analysis/cell_type_markers.csv")
## Parsed with column specification:
## cols(
## `Cell types` = col_character(),
## `Marker genes` = col_character(),
## Reference = col_character()
## )
cell_type_markers
## # A tibble: 6 x 3
## `Cell types` `Marker genes` Reference
## <chr> <chr> <chr>
## 1 Bipolar cells Cabp5, Car10, Grm6, Vsx2, G… Kim et al., 2008 and Park et…
## 2 Rod cells Pde6b, Cngal, Rho, Nr2e3 Kim et al., 2008 and Park et…
## 3 Retinal Ganliion c… Sncg, Nefl, Nefm, Slc17a6, … Laboissonniere et al., 2019
## 4 Amacrine cells Pax6, Gad1, Gad2, Atp1b1 Macosko et al., 2015
## 5 muller_glial_cells Glul, Rlbp1 Laboissonniere et al., 2019
## 6 Horizontal cells Lhx1, Prox1 Boije et al., 2016
Now assigning clusters by known gene markers
Bipolar cells cluster 8 and 3
biplor_cells <-
FeaturePlot(cell_phase_seurat,
reduction = "umap",
features = c("Cabp5", "Car10","Grm6", "Vsx2", "Gabrr1"),
order = TRUE,
min.cutoff = 'q10',
label = TRUE)
biplor_cells
Muller_glial_cells - cluster 4
muller_glial_cells <-
FeaturePlot(cell_phase_seurat,
reduction = "umap",
features = c("Glul", "Rlbp1"),
order = TRUE,
min.cutoff = 'q10',
label = TRUE)
muller_glial_cells
Muller glial cells violin plot
VlnPlot(object = cell_phase_seurat,
features = c("Glul", "Rlbp1"))
Retinal ganglion cell -cluster 5 and 7
retinal_ganglian_cell <-
FeaturePlot(cell_phase_seurat,
reduction = "umap",
features = c("Sncg", "Nefl", "Nefm", "Slc17a6"),
order = TRUE,
min.cutoff = 'q10',
label = TRUE)
retinal_ganglian_cell
Amarcine cells -cluster 2
amacrine_cells <-
FeaturePlot(cell_phase_seurat,
reduction = "umap",
features = c("Pax6", "Gad1", "Gad2", "Atp1b1"),
order = TRUE,
min.cutoff = 'q10',
label = TRUE)
amacrine_cells
Rods cells - big cluster 0,1,6,7,9,10,11,12,13,14,15,16,17
rod_cells <-
FeaturePlot(cell_phase_seurat,
reduction = "umap",
features = c("Pde6b", "Cnga1", "Rho", "Nr2e3"),
order = TRUE,
min.cutoff = 'q10',
label = TRUE)
rod_cells
Rod cells violin plot
VlnPlot(object = cell_phase_seurat,
features = c("Pde6b", "Cnga1", "Rho", "Nr2e3"))
Unable to find horizontal cell cluster
horizontal_cell_cluster <-
FeaturePlot(cell_phase_seurat,
reduction = "umap",
features = c("Lhx1"),
order = TRUE,
min.cutoff = 'q10',
label = TRUE)
horizontal_cell_cluster
In general rod cells represented in very big cluster compared to other cell types
These rod cells clusters such as 9,6,10,11 could be sub-polulation and might have separate gene markers
To find out unqiue gene markers for rod cells small clusters, I am going to use FindAllMarkers function from seurat package
sct_markers <-
FindAllMarkers(cell_phase_seurat,
only.pos = T,
logfc.threshold = 0.25)
## Calculating cluster 0
## Calculating cluster 1
## Calculating cluster 2
## Calculating cluster 3
## Calculating cluster 4
## Calculating cluster 5
## Calculating cluster 6
## Calculating cluster 7
## Calculating cluster 8
## Calculating cluster 9
## Calculating cluster 10
## Calculating cluster 11
## Calculating cluster 12
## Calculating cluster 13
## Calculating cluster 14
## Calculating cluster 15
## Calculating cluster 16
## Calculating cluster 17
Cluster 6
cluster6_markers <-
VlnPlot(object = cell_phase_seurat,
features = c("Gm10800","Gm10801","Gm10717", "Gm21738"))
cluster6_markers
Cluster 9
cluster9_markers <-
VlnPlot(object = cell_phase_seurat,
features = c("Fasn","Ctc1","E330017L17Rik", "Pclo"))
cluster9_markers
Cluster 10
cluster10_markers <-
VlnPlot(object = cell_phase_seurat,
features = c("Pde6h","Opn1sw","Opn1mw", "Gnat2", "Gngt2", "Arr3"))
cluster10_markers
Cluster 11
cluster11_markers <-
VlnPlot(object = cell_phase_seurat,
features = c("Zfp97","Zfp960","Gm5165"))
cluster11_markers
Cluster 12
cluster12_markers <-
VlnPlot(object = cell_phase_seurat,
features = c("Kcnq1ot1")) +
NoLegend()
cluster12_markers
Cluster 13
cluster13_markers <-
VlnPlot(object = cell_phase_seurat,
features = c("Opn1sw"))+
NoLegend()
cluster13_markers
Cluster 14
cluster14_markers <-
VlnPlot(object = cell_phase_seurat,
features = c("Rgs5","Sparcl1"))
cluster14_markers
Cluster specific gene markers should be validated in lab, for now we can assign them as rod cells because of known marker genes
All clusters to respective cell types
cell_phase_seurat <- ## renaming seurat cell types
RenameIdents(object = cell_phase_seurat,
"0" = "Rod cells",
"1" = "Rod cells",
"2" = "Amacrine cells",
"3" = "Bipolar cells",
"4" = "Muller glia",
"5" = "Retinal ganglion cells",
"6" = "Rod cells",
"7" = "Retinal ganglion cells",
"8" = "Bipolar cells",
"9" = "Rod cells",
"10" = "Rod cells",
"11" = "Rod cells",
"12" = "Rod cells",
"13" = "Rod cells",
"14" = "Rod cells",
"15" = "Rod cells",
"16" = "Rod cells",
"17" = "Rod cells")
Final_plot <-
DimPlot(object = cell_phase_seurat,
reduction = "umap",
label = TRUE,
label.size = 3,
repel = TRUE)
Final_plot
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.8.2 AnnotationDbi_1.46.1
## [3] clusterProfiler_3.12.0 RColorBrewer_1.1-2
## [5] pheatmap_1.0.12 biomaRt_2.40.5
## [7] RCurl_1.98-1.2 cowplot_1.0.0
## [9] scales_1.1.1 Matrix_1.2-18
## [11] forcats_0.5.0 stringr_1.4.0
## [13] dplyr_1.0.0 purrr_0.3.4
## [15] readr_1.3.1 tidyr_1.1.0
## [17] tibble_3.0.1 ggplot2_3.3.1
## [19] tidyverse_1.2.1 Seurat_3.1.5
## [21] SingleCellExperiment_1.6.0 SummarizedExperiment_1.14.1
## [23] DelayedArray_0.10.0 BiocParallel_1.18.1
## [25] matrixStats_0.56.0 Biobase_2.44.0
## [27] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0
## [29] IRanges_2.18.3 S4Vectors_0.22.1
## [31] BiocGenerics_0.30.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.7 fastmatch_1.1-0
## [4] plyr_1.8.6 igraph_1.2.5 lazyeval_0.2.2
## [7] splines_3.6.3 listenv_0.8.0 urltools_1.7.3
## [10] digest_0.6.25 htmltools_0.4.0 GOSemSim_2.10.0
## [13] viridis_0.5.1 GO.db_3.8.2 fansi_0.4.1
## [16] magrittr_1.5 memoise_1.1.0 cluster_2.1.0
## [19] ROCR_1.0-11 limma_3.40.6 graphlayouts_0.7.0
## [22] globals_0.12.5 modelr_0.1.8 enrichplot_1.4.0
## [25] prettyunits_1.1.1 colorspace_1.4-1 blob_1.2.1
## [28] rvest_0.3.5 rappdirs_0.3.1 ggrepel_0.8.2
## [31] haven_2.3.0 xfun_0.14 crayon_1.3.4
## [34] jsonlite_1.6.1 survival_3.1-12 zoo_1.8-8
## [37] ape_5.3 glue_1.4.1 polyclip_1.10-0
## [40] gtable_0.3.0 zlibbioc_1.30.0 XVector_0.24.0
## [43] UpSetR_1.4.0 leiden_0.3.3 future.apply_1.6.0
## [46] DOSE_3.10.2 DBI_1.1.0 Rcpp_1.0.4.6
## [49] viridisLite_0.3.0 progress_1.2.2 gridGraphics_0.5-0
## [52] reticulate_1.16 europepmc_0.4 bit_1.1-15.2
## [55] rsvd_1.0.3 tsne_0.1-3 htmlwidgets_1.5.1
## [58] httr_1.4.1 fgsea_1.10.1 ellipsis_0.3.1
## [61] ica_1.0-2 farver_2.0.3 pkgconfig_2.0.3
## [64] XML_3.99-0.3 uwot_0.1.8 utf8_1.1.4
## [67] labeling_0.3 ggplotify_0.0.5 tidyselect_1.1.0
## [70] rlang_0.4.6 reshape2_1.4.4 munsell_0.5.0
## [73] cellranger_1.1.0 tools_3.6.3 cli_2.0.2
## [76] generics_0.0.2 RSQLite_2.2.0 broom_0.5.6
## [79] ggridges_0.5.2 evaluate_0.14 yaml_2.2.1
## [82] knitr_1.28 bit64_0.9-7 tidygraph_1.2.0
## [85] fitdistrplus_1.1-1 RANN_2.6.1 ggraph_2.0.0
## [88] pbapply_1.4-2 future_1.18.0 nlme_3.1-147
## [91] DO.db_2.9 xml2_1.3.2 compiler_3.6.3
## [94] rstudioapi_0.11 curl_4.3 plotly_4.9.2.1
## [97] png_0.1-7 tweenr_1.0.1 stringi_1.4.6
## [100] RSpectra_0.16-0 lattice_0.20-41 vctrs_0.3.1
## [103] pillar_1.4.4 lifecycle_0.2.0 BiocManager_1.30.10
## [106] triebeard_0.3.0 lmtest_0.9-37 RcppAnnoy_0.0.16
## [109] data.table_1.12.8 bitops_1.0-6 irlba_2.3.3
## [112] qvalue_2.16.0 patchwork_1.0.1 R6_2.4.1
## [115] KernSmooth_2.23-17 gridExtra_2.3 codetools_0.2-16
## [118] MASS_7.3-51.6 assertthat_0.2.1 withr_2.2.0
## [121] sctransform_0.2.1 GenomeInfoDbData_1.2.1 mgcv_1.8-31
## [124] hms_0.5.3 grid_3.6.3 rvcheck_0.1.8
## [127] rmarkdown_2.2 Rtsne_0.15 ggforce_0.3.1
## [130] lubridate_1.7.8